CAREER: Single-Atom Alloy Catalyst Design for the Electrocatalytic Reduction of Nitrate to Ammonia: Linking Electronic Structure to Geometry and Catalytic Performance

职业:用于硝酸盐电催化还原为氨的单原子合金催化剂设计:将电子结构与几何结构和催化性能联系起来

基本信息

项目摘要

Nitrate (NO3−) is among the most ubiquitous groundwater pollutants in the world and a serious threat to human and ecosystem health. Thus, there is a compelling need to manage nitrate waste across industry, food, and water systems. The electrocatalytic nitrate reduction reaction (NO3RR) is a promising approach to convert nitrate into valuable ammonia (NH3); however, critical scientific questions and challenges surrounding NO3RR limit its practical use. The proposed research focuses on computationally addressing multiple scientific questions to better understand NO3RR to ammonia on single atom alloy catalysts. This project integrates the research with an educational outreach plan in collaboration with the Museum of Natural History at the University of Michigan and the Washtenaw Community College to promote STEM education and catalysis training.The proposed research focuses on addressing two scientific objectives to enhance NO3RR to ammonia. Prior results showed that the maximum NO3RR activity and selectivity on transition metal electrocatalysts is hindered by linear energy scaling relations (LSRs) between adsorbates. The 1st scientific objective aims to answer mechanistic questions of NO3RR by single-atom alloy (SAAs) electrocatalysts, with the goal to break these LSRs. SAAs are a promising class of catalysts in which small amounts of isolated metal atoms are present in the surface layer of a metal host. Yet SAAs have hardly been explored for NO3RR. Using state-of-the-art Grand Canonical Density Functional Theory, we will test the hypotheses that 1) judiciously selected SAAs will break LSRs that limit NO3RR activity on pure metals, and 2) quench N-N coupling to favor NH3 selectivity. The second scientific objective aims to elucidate how geometry and electronic structure of SAAs link to NO3RR activity and selectivity. These insights will help design SAA catalysts that break LSRs for NO3RR. The expected outcomes of this research are new mechanistic understanding of SAAs and their ability to break LSRs for NO3RR, design rules for SAAs that link their properties to reactivity, and general insights into the role of solvent and applied electrochemical potential on NO3RR. The proposed educational activities are: (i) Creating a “Research Station” museum exhibit that will teach the public about catalysis and the nitrate problem; (ii) Engaging middle school students through a summer science research program; (iii) Teaching and practicing science communication through a Science Communication Fellows Program; and (iv) Serving as summer research mentors to first-generation Community College students. The proposed integrated research and educational activities will support multidisciplinary research training, enhance STEM equity, diversity and inclusion, and increase USA economic competitiveness.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
硝酸盐(NO3−)是世界上最普遍的地下水污染物之一,对人类和生态系统健康构成严重威胁。因此,迫切需要在工业,食品和水系统中管理硝酸盐废物。电催化硝酸盐还原反应(NO3 RR)是将硝酸盐转化为有价值的氨(NH3)的一种有前途的方法;然而,围绕NO3 RR的关键科学问题和挑战限制了其实际应用。拟议的研究重点是计算解决多个科学问题,以更好地了解NO3 RR到氨的单原子合金催化剂。该项目与密歇根大学自然历史博物馆和华盛顿社区学院合作,将研究与教育推广计划相结合,以促进STEM教育和催化培训。拟议的研究重点是解决两个科学目标,以提高NO3 RR到氨。先前的研究结果表明,最大的NO3 RR活性和选择性的过渡金属电催化剂上的吸附质之间的线性能量标度关系(LSR)的阻碍。第一个科学目标旨在通过单原子合金(SAA)电催化剂回答NO3 RR的机理问题,目标是打破这些LSR。SAA是一类有前途的催化剂,其中少量孤立的金属原子存在于金属主体的表面层中。然而,SAAs几乎没有被用于NO3 RR。使用最先进的大正则密度泛函理论,我们将测试以下假设:1)明智选择的SAA将打破限制纯金属上NO3 RR活性的LSR,以及2)淬灭N-N耦合以有利于NH3选择性。 第二个科学目标旨在阐明SAA的几何结构和电子结构如何与NO3 RR活性和选择性联系起来。这些见解将有助于设计SAA催化剂,以打破NO3 RR的LSR。这项研究的预期成果是新的机制的理解SAA和他们的能力,打破LSR的NO3 RR,设计规则的SAA,链接其属性的反应性,和一般的见解溶剂的作用和应用的电化学电位NO3 RR。拟议的教育活动是:(一)创建一个“研究站”博物馆展览,向公众讲授催化和硝酸盐问题;(二)通过暑期科学研究方案吸引中学生参与;(三)通过科学传播研究员方案教授和实践科学传播;以及(四)担任第一代社区学院学生的暑期研究导师。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

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BRYAN GOLDSMITH其他文献

BRYAN GOLDSMITH的其他文献

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{{ truncateString('BRYAN GOLDSMITH', 18)}}的其他基金

EAGER: ADAPT: Hypotheses Generation in Heterogeneous Catalysis using Causal Inference and Machine Learning
EAGER:ADAPT:使用因果推理和机器学习在异质催化中生成假设
  • 批准号:
    2231174
  • 财政年份:
    2022
  • 资助金额:
    $ 57.53万
  • 项目类别:
    Standard Grant

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